SoroushVahidi/scidocs huggingface dataset:
SPIDER: stealthy binary program instrumentation and debugging via hardware virtualization
(2013)

The ability to trap the execution of a binary program at desired instructions is essential in many security scenarios such as malware analysis and attack provenance. However, an increasing percent of both malicious and legitimate programs are equipped with anti-debugging and anti-instrumentation techniques, which render existing debuggers and instrumentation tools inadequate. In this paper, we present Spider, a stealthy program instrumentation framework which enables transparent, efficient and flexible instruction-level trapping based on hardware virtualization. Spider uses invisible breakpoint, a novel primitive we develop that inherits the efficiency and flexibility of software breakpoint, and utilizes hardware virtualization to hide its side-effects from the guest. We have implemented a prototype of Spider on KVM. Our evaluation shows that Spider succeeds in remaining transparent against state-of-the-art anti-debugging and anti-instrumentation techniques; the overhead of invisible breakpoint is comparable with traditional hardware breakpoint. We also demonstrate Spider's usage in various security applications.
Learning to Probabilistically Identify Authoritative Documents
(2000)

We describe a model of document citation that learns to identify hubs and authorities in a set of linked documents such as pages retrieved from the world wide web or papers retrieved from a research paper archive Un like the popular HITS algorithm which re lies on dubious statistical assumptions our model provides probabilistic estimates that have clear semantics We also nd that in general the identi ed authoritative docu ments correspond better to human intuition
Total Anomalous Pulmonary Venous Connection: From Embryology to a Prenatal Ultrasound Diagnostic Update
(2017)

.08.002 C and the Chinese Taipei Society o mons.org/licenses/by-nc-nd/4.0/ also as a part of complex heart diseases or genetic syndromes. Among cyanotic heart diseases, total anomalous pulmonary venous connection (TAPVC) is the only condition involving a venous system malformation [1] and is easily misdiagnosed. The key characteristic of this uncommon congenital heart disease (CHD) is that all four pulmonary veins (PVs) fail to form a direct connection to the left atrium (LA); instead, they drain into the right heart through different routes of systemic venous return. The incidence of this condition is approximately 7e9 per 100,000 live births [2,3], and it accounts for 0.7e1.5% of all CHDs [4]. Patients with CHD who were born from 2000 to 2006 in Taiwan also exhibited a similar prevalence of TAPVC (0.11/ 1000) [5].
Generating Focused Molecule Libraries for Drug Discovery with Recurrent
Neural Networks
(2017)

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.
Impact of Ambulance Dispatch Policies on Performance of Emergency Medical Services
(2011)

In ambulance location models, fleet size and ambulance location sites are two critical factors that emergency medical service (EMS) managers can control to ensure efficient delivery of the system. The ambulance relocation and dispatch policies that are studied in dynamic ambulance relocation models also significantly contribute to improving the response time of EMS. In this paper, we review dynamic ambulance relocation models from the perspective of dispatch policies. The connection between the reviewed ambulance dispatch policies and real-life policies is highlighted. Our ambulance model is based on the modified maximal covering location problem (MCLP). It is used to examine the commonly used dispatch policy and the proposed method of free-ambulance exploitation to further improve urgent call response time. Simulation results show that the proposed method can reduce the response time of urgent calls, especially during low-ambulance-supply period. We also compared the performance of EMS with and without reroute-enabled dispatch.
A trust framework model for situational contexts
(2006)

Trust is a widely studied and acknowledged concept including a diversity of operationalizations. A trust framework model for situational contexts is presented to communicate a clearer and better understanding of the trust situation studied. The framework model includes the situational trust model, the trust transaction, and the trust equation. The situational trust model relates the trustor, the trustee, the trust object, and the trust environment. The application of the trust model is exemplified to define the trust situation of consumer trust in e-commerce.
Towards structured log analysis
(2012)

Value of software log file analysis has been constantly increasing with the value of information to organizations. Log management tools still have a lot to deliver in order to empower their customers with the true strength of log information. In addition to the traditional uses such as testing software functional conformance, troubleshooting and performance benchmarking, log analysis has proven its capabilities in fields like intrusion detection and compliance evaluation. This is verified by the emphasis on log analysis in regulations like PCI DSS, FISMA, HIPAA and frameworks such as ISO 27001 and COBIT. In this paper we present an in depth analysis into current log analysis domains and common problems. A practical guide to the use of few popular log analysis tools is also included. Lack of proper support for structured analysis is identified as one major flaw in existing tools. After that, we describe a framework we developed for structured log analysis with the view of providing a solution to open problems in the domain. The core strength of the framework is its ability to handle many log file formats that are not well served by existing tools and providing sophisticated infrastructure for automating recurring log analysis procedures. We prove the usefulness of the framework with a simple experiment.
An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows
(2006)

Thepickup and delivery problem with time windows is the problem of serving a number of transportation requestsusing a limited amount of vehicles. Each request involves mo ving a number of goods from a pickup location to a delivery location. Our task is to constr uc routes that visit all locations such that corresponding pickups and deliveries are placed on the same rout and such that a pickup is performed before the corresponding delivery. The routes must also sat isfy time window and capacity constraints. This paper presents a heuristic for the problem based on an ex te sion of theLarge Neighborhood Search heuristic previously suggested for solving the vehicle rou ting problem with time windows. The proposed heuristic is composed of a number of competing sub-heuristi cs which are used with a frequency corresponding to their historic performance. This general framework i s denotedAdaptive Large Neighborhood Search . The heuristic is tested on more than 350 benchmark instances with up to 500 requests. It is able to improve the best known solutions from the literature for mor e than 50% of the problems. The computational experiments indicate that it is advantag eous to use several competing sub-heuristics instead of just one. We believe that the proposed heuristic i very robust and is able to adapt to various instance characteristics.
Computing persistent homology
(2004)

We study the homology of a filtered d-dimensional simplicial complex K as a single algebraic entity and establish a correspondence that provides a simple description over fields. Our analysis enables us to derive a natural algorithm for computing persistent homology over an arbitrary field in any dimension. Our study also implies the lack of a simple classification over non-fields. Instead, we give an algorithm for computing individual persistent homology groups over an arbitrary PIDs in any dimension.
Li-Fi technology in traffic light
(2017)

Wireless communication has become a basic utility in personal and business life such that it becomes a fundamental of our lives and this communication uses the radio spectrum for data transfer. There are issues in using the radio spectrum further, they are, capacity, efficiency, availability and security. The defects of Wi-Fi technology has given birth to the concept of Li-Fi (Light Fidelity) technology. Li-Fi can be defined as a light based Wi-Fi. This technology mainly serves the purpose of transmitting data using retrofitting of LED Bulbs that has high efficiency, durability and reliability.This technology emerged by 1990s in a lot of countries like Japan, German and Korea. The proposed paper aims in using the Wi-Fi technology and enabling communication of vehicles with the traffic light system in order to prioritize the vehicles and change the signals accordingly rather than by a process of pre-defined order or by manual order. Traffic lights already use LED lighting, so that this proposed system may seem easy to implement. Sending data through siren lights in an ambulance and fire extinguishers to a traffic light control system and switching the signal in order to allow faster and non-interrupted transport. Here is already a kind of system in play for GPS navigational systems, but the traffic lights can be used in updating drivers using basic information or streaming video directly from news broadcasts i.e, about the accidents or delays up ahead.
A Job Scheduling Design for Visualization Services Using GPU Clusters
(2012)

Modern large-scale heterogeneous computers incorporating GPUs offer impressive processing capabilities. It is desirable to fully utilize such systems for serving multiple users concurrently to visualize large data at interactive rates. However, as the disparity between data transfer speed and compute speed continues to increase in heterogeneous systems, data locality becomes crucial for performance. We present a new job scheduling design to support multi-user exploration of large data in a heterogeneous computing environment, achieving near optimal data locality and minimizing I/O overhead. The targeted application is a parallel visualization system which allows multiple users to render large volumetric data sets in both interactive mode and batch mode. We present a cost model to assess the performance of parallel volume rendering and quantify the efficiency of job scheduling. We have tested our job scheduling scheme on two heterogeneous systems with different configurations. The largest test volume data used in our study has over two billion grid points. The timing results demonstrate that our design effectively improves data locality for complex multi-user job scheduling problems, leading to better overall performance of the service.
[Sensitization to acrylates caused by artificial acrylic nails: review of 15 cases].
(2008)

BACKGROUND
Allergic contact dermatitis due to acrylates present in the workplace is a disease frequently reported among dentists, printers, and fiberglass workers. Recently, the number of cases of contact allergic dermatitis among beauticians specialized in sculpting artificial nails has increased.


OBJECTIVE
Our objective was to study the clinical characteristics and allergens implicated in allergic contact dermatitis due to acrylates in beauticians and users of sculpted nails.


MATERIAL AND METHODS
This was an observational, retrospective study of patients diagnosed with allergic contact dermatitis due to acrylates used in sculpting artificial nails over the last 26 years in the Hospital General Universitario, Valencia, Spain.


RESULTS
In total, 15 patients were diagnosed: 14 beauticians and 1 client. Most cases were diagnosed in the past 2 years. All were women, their mean age was 32.2 years, and 26.7 % had a personal or family history of atopy. The sensitization time varied between 1 month and 15 years. The most frequently affected areas were the fleshy parts of the fingers and hands. Three patients - 2 beauticians and 1 client - presented allergic asthma due to acrylates. All patients underwent patch testing with a standard battery of allergens and a battery of acrylates. The most frequent allergens were ethylene glycol dimethacrylate (13/15, 86.7 %), hydroxyethyl methacrylate (13/15, 86.7 %), triethylene glycol dimethacrylate (7/15, 46.7 %), 2-hydroxypropyl methacrylate (5/15, 33.3 %), and methyl methacrylate (5/15, 33.3 %).


CONCLUSIONS
Acrylate monomers used for sculpting artificial nails are important sensitizers for contact and occupational dermatitis. The most important consideration is primary and secondary prevention.
An Expectation Propagation Perspective on Approximate Message Passing
(2015)

An alternative derivation for the well-known approximate message passing (AMP) algorithm proposed by Donoho is presented in this letter. Compared with the original derivation, which exploits central limit theorem and Taylor expansion to simplify belief propagation (BP), our derivation resorts to expectation propagation (EP) and the neglect of high-order terms in large system limit. This alternative derivation leads to a different yet provably equivalent form of message passing, which explicitly establishes the intrinsic connection between AMP and EP, thereby offering some new insights in the understanding and improvement of AMP.
A Robust Minimax Approach to Classification
(2002)

When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and covariance matrix of each class are assumed to be known. No further assumptions are made with respect to the class-conditional distributions. Misclassification probabilities are then controlled in a worst-case setting: that is, under all possible choices of class-conditional densities with given mean and covariance matrix, we minimize the worst-case (maximum) probability of misclassification of future data points. For a linear decision boundary, this desideratum is translated in a very direct way into a (convex) second order cone optimization problem, with complexity similar to a support vector machine problem. The minimax problem can be interpreted geometrically as minimizing the maximum of the Mahalanobis distances to the two classes. We address the issue of robustness with respect to estimation errors (in the means and covariances of the classes) via a simple modification of the input data. We also show how to exploit Mercer kernels in this setting to obtain nonlinear decision boundaries, yielding a classifier which proves to be competitive with current methods, including support vector machines. An important feature of this method is that a worst-case bound on the probability of misclassification of future data is always obtained explicitly.
A dual-networks architecture of top-down control
(2008)

Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.
Design , construction and verification of a self-balancing vehicle
(2012)

The Segway Personal Transporter is a small footprint electrical vehicle designed by Dean Kamen to replace the car as a more environmentally friendly transportation method in metropolitan areas. The dynamics of the vehicle is similar to the classical control problem of an inverted pendulum, which means that it is unstable and prone to tip over. This is prevented by electronics sensing the pitch angle and its time derivative, controlling the motors to keep the vehicle balancing (1). This kind of vehicle is interesting since it contains a lot of technology relevant to an environmentally friendly and energy efficient transportation industry. This thesis describes the development of a similar vehicle from scratch, incorporating every phase from literature study to planning, design, vehicle construction and verification. The main objective was to build a vehicle capable of transporting a person weighing up to 100 kg for 30 minutes or a distance of 10 km, whichever comes first. The rider controls are supposed to be natural movements; leaning forwards or backwards in combination with tilting the handlebar sideways should be the only rider input required to ride the vehicle. The vehicle was built using a model-based control design and a top-down construction approach. The controller is a linear quadratic controller implemented in a 100 Hz control loop, designed to provide as fast response to disturbances as possible without saturating the control signal under normal operating conditions. The need for adapting the control law to rider weight and height was investigated with a controller designed for a person 1,8 m tall weighing 80 kg. Simulations of persons having weights between 60-100 kg and heights between 1,6-1,9 m were performed, showing no need to adapt the controller. The controller could safely return the vehicle to upright positions even after angle disturbances of ±6 degrees, the highest angle deviation considered to occur during operation.
The "Reading the Mind in the Eyes" Test revised version: a study with normal adults, and adults with Asperger syndrome or high-functioning autism.
(2001)

In 1997 in this Journal we published the "Reading the Mind in the Eyes" Test, as a measure of adult "mentalising". Whilst that test succeeded in discriminating a group of adults with Asperger syndrome (AS) or high-functioning autism (HFA) from controls, it suffered from several psychometric problems. In this paper these limitations are rectified by revising the test. The Revised Eyes Test was administered to a group of adults with AS or HFA (N = 15) and again discriminated these from a large number of normal controls (N = 239) drawn from different samples. In both the clinical and control groups the Eyes Test was inversely correlated with the Autism Spectrum Quotient (the AQ), a measure of autistic traits in adults of normal intelligence. The Revised Eyes Test has improved power to detect subtle individual differences in social sensitivity.
An Experiment on DES Statistical Cryptanalysis
(1996)

Linear cryptanalysis and differential cryptanalysis are the most important methods of attack against block ciphers. Their efficiency have been demonstrated against several ciphers, including the Data Encryption Standard. We prove that both of them can be considered, improved and joined in a more general statistical framework. We also show that the very same results as those obtained in the case of DES can be found without any linear analysis and we slightly improve them into an attack with theoretical complexity 242 ·9 . We can apply another statistical attack the xcryptanalysis on the same characteristics without a definite idea of what happens in the encryption process. It appears to be roughly as efficient as both differential and linear cryptanalysis. We propose a new heuristic method to find good characteristics. It has found an attack against DES absolutely equivalent to Matsui's one by following a distinct path.
Real-Time Tracking via On-line Boosting
(2006)

Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost feature selection algorithm for tracking. The distinct advantage of our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object (e.g. out of plane rotations, illumination changes) are handled quite naturally. Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences.
Incremental Learning for Robust Visual Tracking
(2007)

Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object’s appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.